Developer shares 5 real deployment failures from 6 live AI projects on Streamlit Cloud

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A developer deployed six AI projects from GitHub to Streamlit Cloud and encountered five distinct failures that never appeared in local testing. Among the issues were a broken LangChain import caused by unpinned package versions pulling a newer API during a clean cloud install, and a FAISS index crash traced to Git LFS storing only a pointer file instead of the actual binary. A 83MB PyTorch model checkpoint also failed to upload via GitHub's web interface, which enforces a 25MB limit separate from the 100MB limit allowed through command-line git push. The developer notes that local environments often mask these problems because they retain cached or older package versions that a fresh deployment environment never inherits. The key takeaways include pinning dependency versions before deploying, avoiding Git LFS on platforms that may not support it, and using command-line tools rather than web interfaces for large file uploads.

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